Statistical synopses for graph-structured XML databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Counting Twig Matches in a Tree
Proceedings of the 17th International Conference on Data Engineering
Estimating the Selectivity of XML Path Expressions for Internet Scale Applications
Proceedings of the 27th International Conference on Very Large Data Bases
XPathLearner: an on-line self-tuning Markov histogram for XML path selectivity estimation
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Counting graph matches with adaptive statistics collection
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
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Efficient and accurate selectivity estimation in graph-structured data, specifically for complex branching path query, is becoming a challenging and all-important problem for query performance optimization. Precise and flexible statistics summarization about graph-structured data plays a crucial role for graph query selectivity estimation. We propose DMT, Dynamic Markov Table, which is a dynamic graph summarization based on Markov Table by applying flexible combination of 4 Optimized Rules which investigate local forward and backward inclusions. The efficient DMT construction algorithm DMTBuilder and DMT-based statistical methods are introduced for selectivity estimations of various graph queries. Our extensive experiments demonstrate the advantages in accuracy and scalability of DMT by comparing with previously known alternative, as well as the preferred Optimized Rules that would favor different situations.